A Study of Team Payroll and Performance in the MLB
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Can Money Buy Success?: A Study of Team Payroll and Performance in the MLB Shahriar Hasan, Thompson Rivers University, Canada ABSTRACT Using the last 16 years of winning percentages and pay-scales of the MLB teams, I investigated the relationship between team performance and payroll. I found strong evidence that during the regular season games, the payroll plays a significant positive role on the performance. Although the results of the individual team studies were not conclusive, the results were prominent when all teams are included in the data. Different variations of the pooled data were used to test the robustness of the outcome. I also found weak evidence that being a new team in the MLB may have negative effects on performance. INTRODUCTION In his 2002 eye-opening book Moneyball, Michael Lewis asked the question: “if the teams with the highest salaries consistently do well, why are the Oakland Athletics always one of the best teams with one of the lowest payrolls”? In 2001, when the Arizona Diamondbacks won the MLB crown being the 7th largest spender in the league and in only their 4th year of operations, it was understood that team payrolls could not be the only determinant of success. While a considerable amount of research has been done on the relationship between individual player salaries and respective performances, few studies have sought to examine the larger and more encompassing issue of team payrolls as the deciding factor of overall team performance. This paper, following the line of reasoning by Hall, Szymanski & Zimbalist (2002), attempts to establish a relationship between team payrolls and team performance in the Major League Baseball using 1992-2007 data. In 2005-06, the minimum salary for a player in the MLB was US$327,000 while the average was an astounding US$2,476,589. With that kind of money circulating, one would reasonably assume that the majority of the players are sufficiently motivated to give their best efforts. Furthermore, since the inception of the free agency system in 1976, players have had the freedom to move freely between clubs, thus giving one reason to believe that highest spenders would accumulate the most amount of talent. In a world of free movement and free information, teams with the highest payrolls should consistently win. BACKGROUND One of the earliest researchers in this field, Rottenberg (1956) focused on mostly why and how playing talent would be more or less equally distributed among teams given that teams would want to win closely contested games. Before the introduction of the free agency system, Scully (1974), in his seminal article, investigated the issue of individual players’ wages against their estimated marginal revenue product. He found the wages to be significantly lower than their respective MRPs. Zimbalist (1992) used a modified Scully method to dispense the worries about this inequality – he basically showed that the 1976 introduction of free agency system almost eliminated the monopsony powers of the team owners and brought the wages up to their MRP levels. Scully’s 1989 model have been scrutinized by others in different ways. Sommers & Quinton (1982) formalized a team revenue function and showed that each extra win contributes more marginal revenue in larger markets than smaller ones. This is a result that the Zimbalist (1992) study did not find. Before the publication of Zimbalist’s article, Scully came out with another path breaking work on pay and performance in baseball. In his 1989 book, he drew the conclusion that team revenues are directly related to the club’s win percentage and that the size of the home market also positively affects team revenues. But Quirk & Fort (1999) argued that under free agency teams should get pretty much what they paid for. They looked at the correlation between the rank of regular season winning percentages and the rank of player payroll cost for the years of 1990 to 1997. Their findings were that this correlation was 0.509 in the American League and 0.135 in the National League. However, none of these were statistically significant and thus they concluded that payrolls could not explain the team performance in baseball. The 1992 Zimbalist study also lent support to this notion that team performance and payroll are not strongly correlated. Finally, Hall, Szymanski and Zimbalist (2002) used a 20 year data set to examine this link in both MLB and English Soccer. Additionally, they also tested for causality – whether the relationship runs from payroll to performance or vice versa. They were able to demonstrate that the correlation between performance and payroll increased significantly during the 1990s as compared to the 1980s, however, they failed to establish the direction of causality for the entire period. In a related study, Burger and Walters (2003) proved the existence of market size effect on expected team performance. They showed that ceteris paribus, baseball teams in the largest markets will value a given player six times more than those in the smallest. Also, within each market, achieving contending status can raise a player’s value significantly. THE MODEL For this paper, a definition of success has been adopted which does not extend in to the playoff season. It is assumed that the 162 games played during the regular season give us a more accurate picture of success of a team. The ranking of a team in regular season was not used as the indicator of success. Clearly illustrating the inherent problem associated with such an approach, the difference between a team ranked 5th and a team ranked 15th, is not necessarily by a factor of three. Thus, in terms of team spending, unless the 5th rank team were to spend three times the amount of the 15th rank team, analysis would always suggest a non-relation or not-so-strong relation. In this study, the percentage of wins during the regular season was chosen as the mark of success. On the determinant side, the team payroll is the lonely variable. A seasonally adjusted figure would not have done justice to the study since the increase in payment would not be accompanied by year to year increases in the winning percentage. Instead, the team payroll variable was standardized by dividing each team’s yearly payroll by the average of that year’s league payroll. Not only did this bring harmony in the analysis, but this aids the reader to see which teams were spending more/less than the average on a yearly basis. Using the equation: Win Pctt = α + β Pay-scalet + εt, a relationship was examined between pay and performance in both the team and the overall (pooled) level. The pay-scale in the equation is the percentage of the actual team payroll compared to the average payroll for the overall league for the year. For both levels of study, a simple OLS was run to see whether β is significantly positive or not. A decision was made to add a dummy variable that covered the first three years of operations for the four newcomer teams – Colorado, Florida, Arizona and Tampa Bay. (The first two started operation in 1993 while the latter two in 1998). The reason for introducing the dummy variable was to allow for the effects of initial internal adjustments on the teams’ performances. Besides the addition of the four teams as mentioned above, the MLB also allowed two teams to relocate – the California Angels became Anaheim Angels in 1997 and the Montreal Expos relocated to Washington DC as the Nationals in 2003. It was assumed that these changes did not affect the team performance drastically, as both teams kept their management and players pretty much unchanged during the transition. While running the pooled level tests, the effect of the new club status was examined to see if it has any negative impact on the winning percentage or not. Using a proxy variable for the new teams for their first three years of operation, the following equation Win Pctt = α + β Pay-scalet + λ Dummyt + εt, was used to test whether β is significantly positive and if λ, significantly negative. DATA COVERAGE The relationship between payroll and performance was investigated, using both pooled and individual team data for Major League Baseball during the period 1992 to 2007. This particular period was selected so that the market uncertainties before the 1994-95 MLBPA strike could be minimized over the entire period. The data is easily available through a multitude of electronic and published sources. For team level studies, pay-scale averages for each team were computed to give a historical snapshot of who had been the big spenders during the last 16 years. On top of this, an assumption was made that spending more than 100% of the average payroll should bring 50% or more wins for the teams. Thus, if a team spent more than 100% but won less than 50%, it was termed as an underachievement. Similarly if a team spending less than 100% won more than 50%, it was considered to an overachievement. When using the pooled data to run the regressions, the entire league was divided into two groups - the first with payroll figures being greater than the 100% of the average payroll; and the second with less than 100%. A similar division was also performed from the winning perspective – the first group had a 50% or greater winning percentage, and the second group of less than 50%. This division allowed an objective assessment of whether the pay-scale effect is uniformly applicable at all levels. To be a little more precise, the above mentioned divisions were further analyzed and sub-divided into three groups: on the pay-scale basis, the first group had 120% or more, the second one had 80 to 120% and the third group had less than 80% of the pay-scale.